Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Here's a comparison i never thought i would make Qwen-3.8-27b vs Gemini-3.7-flash Yes, you read it right! I don't know what to say, this is soo damn impressive. the difference is hugeee🤯 Take a look at this result 😍 (watch it in highest quality) Same prompt, both one shot... show more

AJ

3,414 subscribers

273,466 Aufrufe • vor 18 Tagen •via X (Twitter)

Wissenschaft & Technologie Comedy Bildung

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Alright guys, wow. Qwen 3.8 27B is basically Qwen 4. What we have here is one of the best one-shot shark survival games I've made with any model. I ran this same prompt through fable, and the biggest thing all models struggle with in this test is the top-down view of the sharks. Fable even had all of the fins reversed and details jumbled on the first turn. Before now, it was really a 2 to 8 turn test to get something final regardless of model. Qwen 3.8 27B just nailed this in one output, no harness at all, just purely the model thoroughly thinking before outputting the final result. On every asset used, different shark variants, everything, it all looks incredibly tight. The boat movement, shark speed and engagement, everything is so much better than I expected, and I was expecting a lot. Imagine what 8 turns would do in a harness? This confirms to me that while Qwen 3.8 27B thinks a lot, for the first time in a local model on the base weights, IT MIGHT ACTUALLY BE WORTH IT This is a Frontier Lab model result, and it ran on my 5090 at 60tps in Q5_K_M. I am just in complete astoundment at what Qwen has accomplished here, and I am incredibly grateful that this model is open source. The speed will also increase drastically with optimisations. This is just an incredible day. What a blessing. More to come!

Alright guys, wow. Qwen 3.8 27B is basically Qwen 4. What we have here is one of the best one-shot shark survival games I've made with any model. I ran this same prompt through fable, and the biggest thing all models struggle with in this test is the top-down view of the sharks. Fable even had all of the fins reversed and details jumbled on the first turn. Before now, it was really a 2 to 8 turn test to get something final regardless of model. Qwen 3.8 27B just nailed this in one output, no harness at all, just purely the model thoroughly thinking before outputting the final result. On every asset used, different shark variants, everything, it all looks incredibly tight. The boat movement, shark speed and engagement, everything is so much better than I expected, and I was expecting a lot. Imagine what 8 turns would do in a harness? This confirms to me that while Qwen 3.8 27B thinks a lot, for the first time in a local model on the base weights, IT MIGHT ACTUALLY BE WORTH IT This is a Frontier Lab model result, and it ran on my 5090 at 60tps in Q5_K_M. I am just in complete astoundment at what Qwen has accomplished here, and I am incredibly grateful that this model is open source. The speed will also increase drastically with optimisations. This is just an incredible day. What a blessing. More to come!

Kyle Hessling

24,917 Aufrufe • vor 18 Tagen

Every big MoE model I test on my 3090 is basically to see how good it is compared to Qwen3.6-27B. "How good is it compared to Qwen3.6-27B?" is the question I get asked the most, and that shows how much people love and, more importantly, trust the 27B 27B is battle tested. It has proven its worth and has nothing left to prove. Now it just needs to make way for the next 27B. Here’s an example video: a Three.js task, same prompt, not oneshot but two shots for all models. All ran on my 3090 + 64GB VRAM Qwen-3.6-27b fully on vram while others have experts offloaded to system ram. Pinned on the left: Qwen3.6-27B-Q5 On the right, i'm switching between Inkling-Small-Q4_K_M DSV4-Flash-0731-UD-IQ1 Ling-3.0-Flash-Q4_K_M Qwen3.6-27B nailed it. The effects and visuals are just spot on.The other models got some things right, but not everything. Second place would have to go to Inkling-Small while DSV4-Flash-0731 and Ling-3.0-Flash failed to capture the full requirements. This is also why we’re all excited for Qwen Qwen3.8-27B!

Every big MoE model I test on my 3090 is basically to see how good it is compared to Qwen3.6-27B. "How good is it compared to Qwen3.6-27B?" is the question I get asked the most, and that shows how much people love and, more importantly, trust the 27B 27B is battle tested. It has proven its worth and has nothing left to prove. Now it just needs to make way for the next 27B. Here’s an example video: a Three.js task, same prompt, not oneshot but two shots for all models. All ran on my 3090 + 64GB VRAM Qwen-3.6-27b fully on vram while others have experts offloaded to system ram. Pinned on the left: Qwen3.6-27B-Q5 On the right, i'm switching between Inkling-Small-Q4_K_M DSV4-Flash-0731-UD-IQ1 Ling-3.0-Flash-Q4_K_M Qwen3.6-27B nailed it. The effects and visuals are just spot on.The other models got some things right, but not everything. Second place would have to go to Inkling-Small while DSV4-Flash-0731 and Ling-3.0-Flash failed to capture the full requirements. This is also why we’re all excited for Qwen Qwen3.8-27B!

AJ

11,670 Aufrufe • vor 24 Tagen

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

20,335 Aufrufe • vor 4 Tagen

Qwen 3.8 27B beat Claude Opus 4.6 for $0 open-weights Qwen 3.8 27B on one rented GPU vs anthropic's Claude Opus 4.6. same prompt, no edits the test: one self-contained three.js file rendering sunken Atlantis — temple, columns, boid fish, kelp, bubbles, god rays, caustics — plus a 20-second camera flythrough cost: Qwen 3.8 27B — free (open weights, self-hosted) Claude Opus 4.6 — $0.21 what we saw: • qwen actually built a temple — full colonnade, domed roof, glowing portal centered. opus made a long row of columns fading into fog • qwen's portal is the star of the shot. opus leaves it small and off to the side • qwen packed more into frame — fish, kelp, ruins layered together. opus spreads thin and empty qwen's weights are on hugging face — you can run this one yourself!

Qwen 3.8 27B beat Claude Opus 4.6 for $0 open-weights Qwen 3.8 27B on one rented GPU vs anthropic's Claude Opus 4.6. same prompt, no edits the test: one self-contained three.js file rendering sunken Atlantis — temple, columns, boid fish, kelp, bubbles, god rays, caustics — plus a 20-second camera flythrough cost: Qwen 3.8 27B — free (open weights, self-hosted) Claude Opus 4.6 — $0.21 what we saw: • qwen actually built a temple — full colonnade, domed roof, glowing portal centered. opus made a long row of columns fading into fog • qwen's portal is the star of the shot. opus leaves it small and off to the side • qwen packed more into frame — fish, kelp, ruins layered together. opus spreads thin and empty qwen's weights are on hugging face — you can run this one yourself!

AI/ML API

102,960 Aufrufe • vor 18 Tagen

Qwopus 3.6-27b Coder I had a lot of requests to test it, so I did. I ran the same tests I’ve done on other models. It scored lower on tool-eval-bench than both Qwen 3.6 27b and Qwen 3.6 35b. It also produced broken single-file Tetris and Solar System HTML demos, even after 3 fresh attempts. In the Tetris html, the game stalled when you completed a line. It also randomly changed the pieces when moving them left or right. In the Solar System html, clicking on the sun or any planet broke the rendering — the objects just disappeared. Conclusion: The regular Qwen 3.6 27b and 35b models performed better. What do you think of Qwopus 3.6-27b Coder? Full results & RAW files: Full prompts in the posts below.

Qwopus 3.6-27b Coder I had a lot of requests to test it, so I did. I ran the same tests I’ve done on other models. It scored lower on tool-eval-bench than both Qwen 3.6 27b and Qwen 3.6 35b. It also produced broken single-file Tetris and Solar System HTML demos, even after 3 fresh attempts. In the Tetris html, the game stalled when you completed a line. It also randomly changed the pieces when moving them left or right. In the Solar System html, clicking on the sun or any planet broke the rendering — the objects just disappeared. Conclusion: The regular Qwen 3.6 27b and 35b models performed better. What do you think of Qwopus 3.6-27b Coder? Full results & RAW files: Full prompts in the posts below.

Mia

11,183 Aufrufe • vor 2 Monaten

I'm really happy with Qwen3.8-27B ! All frustrations of not able to use Ox-Alpha and running out of credits for Glm-5.3, Grok turned me into Qwen-3.8-27b. Running locally on my 3090. This is the first fps game i'm creating with Qwen3.8-27b. I was after NMS vibe, alien planet, vast, flying drones, background music + sound effects. Without any external assets at all. With proper game like graphics settings panel 😍 And i'm really impressed, because all this is generated with my mixed quant for Qwen3.8-27B, which gives me more context + speed for a really good quality work. DSK harness at beginning and Pi agent towards the end cause i wanted to get this done fast. Full quant name: HF aj9o9/Qwen3.8-27B-GGUF file name: Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf Just like all of you, i also hate having to compact context, so higher context without ever needing to compromise quality is what i'm always after and this quant is created for that. Gives me 210K context at q8_0 kv + dflash2 Q4_K_M for speculative decoding. This result in 23GB vram usage.

I'm really happy with Qwen3.8-27B ! All frustrations of not able to use Ox-Alpha and running out of credits for Glm-5.3, Grok turned me into Qwen-3.8-27b. Running locally on my 3090. This is the first fps game i'm creating with Qwen3.8-27b. I was after NMS vibe, alien planet, vast, flying drones, background music + sound effects. Without any external assets at all. With proper game like graphics settings panel 😍 And i'm really impressed, because all this is generated with my mixed quant for Qwen3.8-27B, which gives me more context + speed for a really good quality work. DSK harness at beginning and Pi agent towards the end cause i wanted to get this done fast. Full quant name: HF aj9o9/Qwen3.8-27B-GGUF file name: Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf Just like all of you, i also hate having to compact context, so higher context without ever needing to compromise quality is what i'm always after and this quant is created for that. Gives me 210K context at q8_0 kv + dflash2 Q4_K_M for speculative decoding. This result in 23GB vram usage.

AJ

11,847 Aufrufe • vor 10 Tagen

This happens all the time! This is why I feel like I'm not worth myself! This is why I feel like I cant grow! This is twitch actively nerfing my account every time I go live! The audience didn't leave they were still in the chat! When I asked twitch for help, I got an automated incomplete response that said "everything looks" . I am so tired of fighting this and have pleaded with twitch, Daniel Clancy and have told the VP FACE TO FACE about this issue and still nothing has been done! Why? I'm small potatoes no one cares and no one will even put an iota of time into solving this issue! This has been a constant every day for over 2 years and I cannot express how defeated I absolutely feel every time I press start to immediately have my audience ignored by twitch. I know some of you are going to say its not all about the numbers and if you enjoy it, it shouldn't matter. Unfortunately the ultimate truth is we are heavily judged on our CPM by everyone, potential partners, sponsors, collaborative opportunities and even by twitch themselves. So yes, it does matter. It matters a lot and worst part is no one, will take this issue seriously! I have literally 120 days of information from twitches 9th longest subathon (unrecognized of course as again small potatoes) that would absolutely show this being a consistent issue. I don't know what to do, I am at wits end here and I every time I see this happen to me I just want to curl up into a ball and let the world wash over me. I need help, i need someone to look at this, because I have dedicated my life to entertaining individuals and I do this full time, I dedicate 8 hours a day every day for the past 2 years to this company and the only thing I get is ignored. I know this is a lot and it looks like just some random person complaining, but I need some way to get this in the right hands and not the automated jarble that I've been getting. Its even been acknowledged in the past by a tech for twitch that something might be up. Again, I know I'm not big but were all working under the same logo and should all be taken care of equally! Twitch

Frosted Fricks 💣🐦‍⬛

21,589 Aufrufe • vor 1 Jahr

Seedance 2.0 vs Gemini Omni, tested under the same conditions 👀 I was working on a new storyboard. After generating it with Seedance 2.0, I gave the exact same prompt + storyboard reference + character reference to Gemini Omni as well. Result: Gemini Omni surprised me with style quality and got closer than I expected in prompt adherence. But Seedance still feels ahead for storyboard execution, motion energy, camera language and environmental interaction. Gemini looks good. Seedance feels directed. You can see them in the video and the storyboard sheet is shown at the end. Which one do you think is more useful for this kind of work?

Seedance 2.0 vs Gemini Omni, tested under the same conditions 👀 I was working on a new storyboard. After generating it with Seedance 2.0, I gave the exact same prompt + storyboard reference + character reference to Gemini Omni as well. Result: Gemini Omni surprised me with style quality and got closer than I expected in prompt adherence. But Seedance still feels ahead for storyboard execution, motion energy, camera language and environmental interaction. Gemini looks good. Seedance feels directed. You can see them in the video and the storyboard sheet is shown at the end. Which one do you think is more useful for this kind of work?

Kōda

36,015 Aufrufe • vor 3 Monaten

MTPLX V2.10 is out! I put a lot of work into this one. Highlights: - Qwen 3.8 Next Support Added: Bare Speed (4 bit) and Optimised Speed (4 bit/8 bit) models added running at 50 - 80 TPS sustained at high contexts. (expect speed improvements in the coming days) - Qwen 3.8 Next mmap: The 32 GB n-gram table streams from SSD via mmap instead of RAM. Both models fit a 96 GB Mac with 0 speed impact. - Qwen 3.8 27B Decode & Prefill boost EVERYWHERE: +15% TPS at 3k context, +29% at 88k, +54% at 147k. Prefill +41%. - 48 GB Macs went from 3 to 4 TPS in swap @ 30k context to 33 TPS. - KV Cache has been solved!: 8 bit and 4 bit cache now barely impacts speed instead of a 50% decline. - Coding agents wall time optimisations: file task dropped from 150s to 44s, and mid-session first token went from ~2s to 0.11s. - CLI UX improvements + 34 bug fixes. My thoughts on Qwen Next? Incredible model. Enormously better at design, and creative tasks with scary good vision. It created what I think is the best flappy bird. maybe even better than the Fable version. It had various difficulties and skins, moving pillars, and upgrades. It also had the most memeified look. Incredibly impressed. However, it is slightly worse than 27B at having code just work straight away. But it's personality is also far better than 27B. Here is the Flappy bird it made below! Made with Qwen Next Optimized Speed one shot@ 57 TPS:

MTPLX V2.10 is out! I put a lot of work into this one. Highlights: - Qwen 3.8 Next Support Added: Bare Speed (4 bit) and Optimised Speed (4 bit/8 bit) models added running at 50 - 80 TPS sustained at high contexts. (expect speed improvements in the coming days) - Qwen 3.8 Next mmap: The 32 GB n-gram table streams from SSD via mmap instead of RAM. Both models fit a 96 GB Mac with 0 speed impact. - Qwen 3.8 27B Decode & Prefill boost EVERYWHERE: +15% TPS at 3k context, +29% at 88k, +54% at 147k. Prefill +41%. - 48 GB Macs went from 3 to 4 TPS in swap @ 30k context to 33 TPS. - KV Cache has been solved!: 8 bit and 4 bit cache now barely impacts speed instead of a 50% decline. - Coding agents wall time optimisations: file task dropped from 150s to 44s, and mid-session first token went from ~2s to 0.11s. - CLI UX improvements + 34 bug fixes. My thoughts on Qwen Next? Incredible model. Enormously better at design, and creative tasks with scary good vision. It created what I think is the best flappy bird. maybe even better than the Fable version. It had various difficulties and skins, moving pillars, and upgrades. It also had the most memeified look. Incredibly impressed. However, it is slightly worse than 27B at having code just work straight away. But it's personality is also far better than 27B. Here is the Flappy bird it made below! Made with Qwen Next Optimized Speed one shot@ 57 TPS:

Youssof Al Toukhi

20,078 Aufrufe • vor 3 Tagen

okay, this is scary good.. i just found the Seedance 2.5 Prompt Collection and went in looking for one idea to try. now i have 20+. i picked one prompt, put my own spin on it, and made this. the best part is that every time you scroll, you find another idea you want to try. if you want to fall down the same rabbit hole:

okay, this is scary good.. i just found the Seedance 2.5 Prompt Collection and went in looking for one idea to try. now i have 20+. i picked one prompt, put my own spin on it, and made this. the best part is that every time you scroll, you find another idea you want to try. if you want to fall down the same rabbit hole:

Amit

27,993 Aufrufe • vor 3 Tagen

hey here is the final result of octopus invaders on nvidia's flagship at full precision. nemotron super 120B on 2x H200 NVL. BF16 unquantized. 287GB of VRAM. hermes agent as the harness. 60 tok/s. first try it autonomously coded for 6 minutes straight. created 11 files. correct project structure. correct load order. started the server. i opened the browser and the result was a blank screen. i did not give up. second try i gave it a precise list of bugs and things to fix. it went back in for another 3 minutes. patched the code. served it again. still blank. so i did what any sane person would do. third try i just said the screen is blank, test it and fix it yourself. and this is where nemotron showed what it actually is. it became a debugger. you can see it in the video. realtime CSS test squares, red screen flashes, hermes agent browser tools, inspecting its own output. it built the parallax background with planets and comets. it rendered a rocket ship that tracks your mouse with fire and bullet physics. the aesthetic is real. but no enemies spawn. no collision. not playable. what surprised me is qwen 27B one shotted this exact game on a single RTX 3090 at Q4 quant. and here is nvidia's flagship at full precision on enterprise hardware needing 3 tries and still not getting there. that makes my hope high for the undisputed qwen 122B which is about to face the same test next. same hardware. same prompt and same harness. lets see if it one shots or not. full session in the video. no cuts. 5x speed.

hey here is the final result of octopus invaders on nvidia's flagship at full precision. nemotron super 120B on 2x H200 NVL. BF16 unquantized. 287GB of VRAM. hermes agent as the harness. 60 tok/s. first try it autonomously coded for 6 minutes straight. created 11 files. correct project structure. correct load order. started the server. i opened the browser and the result was a blank screen. i did not give up. second try i gave it a precise list of bugs and things to fix. it went back in for another 3 minutes. patched the code. served it again. still blank. so i did what any sane person would do. third try i just said the screen is blank, test it and fix it yourself. and this is where nemotron showed what it actually is. it became a debugger. you can see it in the video. realtime CSS test squares, red screen flashes, hermes agent browser tools, inspecting its own output. it built the parallax background with planets and comets. it rendered a rocket ship that tracks your mouse with fire and bullet physics. the aesthetic is real. but no enemies spawn. no collision. not playable. what surprised me is qwen 27B one shotted this exact game on a single RTX 3090 at Q4 quant. and here is nvidia's flagship at full precision on enterprise hardware needing 3 tries and still not getting there. that makes my hope high for the undisputed qwen 122B which is about to face the same test next. same hardware. same prompt and same harness. lets see if it one shots or not. full session in the video. no cuts. 5x speed.

Sudo su

11,011 Aufrufe • vor 5 Monaten

Ox Alpha : flash multimodal This is one shot loop i ask it to make a loop of pelican riding bike and open his phone where the same gif is looping This is benefit of multimodality and now we will have good opensource model with it

Ox Alpha : flash multimodal This is one shot loop i ask it to make a loop of pelican riding bike and open his phone where the same gif is looping This is benefit of multimodality and now we will have good opensource model with it

Chetaslua

13,947 Aufrufe • vor 9 Tagen

🚨Gemini 3.0 Pro - One shotted i asked it for retro nintendo sims with games and no external assets used , it takes 1 min to output no other model have this level of consistency Prompt and Proof of one shot in Thread and this is not the best shot , there is one more cooking with slightly different prompt

🚨Gemini 3.0 Pro - One shotted i asked it for retro nintendo sims with games and no external assets used , it takes 1 min to output no other model have this level of consistency Prompt and Proof of one shot in Thread and this is not the best shot , there is one more cooking with slightly different prompt

Chetaslua

184,281 Aufrufe • vor 10 Monaten

DeepSeek v4 Flash (0731) vs Kimi K3 (10x more weights) i gave both the exact same prompt, in the exact same harness: one-shot a luxury product landing page ($10,000 "ARC-01" concept object). results in the video. my take: Kimi K3: wins on polish. Better typography hierarchy, more confident use of negative space, and the hero product shot integration is genuinely beautiful DeepSeek v4 Flash: very close. The structure is right, the copy is right, the layout logic is right. It loses on finish, not on fundamentals. a 10x smaller model getting ~90% of the way there. a year ago this gap would have been embarrassing. mind you it cost less than $0.10 for 6 build iterations and 8 QA passes. small models aren't "good for their size" anymore. they're just good.

DeepSeek v4 Flash (0731) vs Kimi K3 (10x more weights) i gave both the exact same prompt, in the exact same harness: one-shot a luxury product landing page ($10,000 "ARC-01" concept object). results in the video. my take: Kimi K3: wins on polish. Better typography hierarchy, more confident use of negative space, and the hero product shot integration is genuinely beautiful DeepSeek v4 Flash: very close. The structure is right, the copy is right, the layout logic is right. It loses on finish, not on fundamentals. a 10x smaller model getting ~90% of the way there. a year ago this gap would have been embarrassing. mind you it cost less than $0.10 for 6 build iterations and 8 QA passes. small models aren't "good for their size" anymore. they're just good.

filipe

26,175 Aufrufe • vor 1 Monat

Caitlin Clark on how she gets covered by the media: "I think there's just like a false characterization of who I am sometimes. And I get it like as a reporter or as a blogger, like you can write an opinion piece and that's your job and whatever. But at the same time, like you don't know me. Like we've never had a conversation. I get people have a job to do. But at the same time, like don't make accusations about my character and who I am when you don't know me. I understand this is your job and this is your profession, and this is what you get to do. And basketball is my profession, what I get to do. And the reason I play it is when I step in between the lines, I love it. And that's what's so fun about it for me is like, I get to compete. This was always my dream. And nobody should ever try to take that away from me"

Caitlin Clark on how she gets covered by the media: "I think there's just like a false characterization of who I am sometimes. And I get it like as a reporter or as a blogger, like you can write an opinion piece and that's your job and whatever. But at the same time, like you don't know me. Like we've never had a conversation. I get people have a job to do. But at the same time, like don't make accusations about my character and who I am when you don't know me. I understand this is your job and this is your profession, and this is what you get to do. And basketball is my profession, what I get to do. And the reason I play it is when I step in between the lines, I love it. And that's what's so fun about it for me is like, I get to compete. This was always my dream. And nobody should ever try to take that away from me"

Oh No He Didn't

260,949 Aufrufe • vor 1 Monat

Let's talk Chiefs run game and RPOs, volume up on this. There's a lot I don't discuss TBF, but this is about what the lack of a consistent run game allows a defense to do and what it takes away from the players. This is objectively a great play/result! But is it best for this O?

Let's talk Chiefs run game and RPOs, volume up on this. There's a lot I don't discuss TBF, but this is about what the lack of a consistent run game allows a defense to do and what it takes away from the players. This is objectively a great play/result! But is it best for this O?

Mitchell Schwartz

51,594 Aufrufe • vor 9 Monaten

Gemini 3.5 Pro vs Grok 4.6 Please read entire post before coming to conclusion :) Both are present in arena first of all let me clear i really want that google cook great comeback but this model is disaster it make stuff that you didnot asked for , have crappy ui guys at this point we have grok with better ui compared to gemini > gemini was good in webdev now its bad > gemini 2.5 DT was good in 3d now its bad > gemini was less restrictive and prompt following in 2.5 pro was so good , now its corporate slop with template , at this time you will feel that there is template for coding , writing and researching all sucks I will write a long review if you guys want that , this is first time i am writing long tweet , i will now follow this for every big release

Gemini 3.5 Pro vs Grok 4.6 Please read entire post before coming to conclusion :) Both are present in arena first of all let me clear i really want that google cook great comeback but this model is disaster it make stuff that you didnot asked for , have crappy ui guys at this point we have grok with better ui compared to gemini > gemini was good in webdev now its bad > gemini 2.5 DT was good in 3d now its bad > gemini was less restrictive and prompt following in 2.5 pro was so good , now its corporate slop with template , at this time you will feel that there is template for coding , writing and researching all sucks I will write a long review if you guys want that , this is first time i am writing long tweet , i will now follow this for every big release

Chetaslua

39,085 Aufrufe • vor 26 Tagen

0xSero helped me setup local models properly and I uh, had no idea these things had gotten this good Are they frontier level? No, but considering this is running on just my 5090 it's remarkably capable First tests on a couple of programming tasks and the qwen 3.6-27b model with no reasoning feels about on par with something like sonnet 4-ish, probably better it's really impressive But also setting up local models isn't easy, I don't know nearly enough to talk much about it yet other than you need to know what you're doing to have a good experience. The out of the box stuff is not nearly as good as setting it up correctly

0xSero helped me setup local models properly and I uh, had no idea these things had gotten this good Are they frontier level? No, but considering this is running on just my 5090 it's remarkably capable First tests on a couple of programming tasks and the qwen 3.6-27b model with no reasoning feels about on par with something like sonnet 4-ish, probably better it's really impressive But also setting up local models isn't easy, I don't know nearly enough to talk much about it yet other than you need to know what you're doing to have a good experience. The out of the box stuff is not nearly as good as setting it up correctly

Ben Davis

14,490 Aufrufe • vor 3 Monaten

hey if you're thinking about running qwopus (the claude opus distilled qwen 3.5 27B) as a coding agent, this might save you a few hours. i tested both the base and the distilled version on the same hardware. single RTX 3090. same prompt. same context. same everything. the only variable was the model weights. base qwen 3.5 27B built octopus invaders in 13 minutes. 1,827 lines across 11 files. zero steering. one scope bug that took 2 lines to fix. game ran. qwopus couldn't finish the same task. enemies overlapping on screen. bullets not firing. controls worked but the game was broken. i had to steer it multiple times and it still didn't produce a playable result. both run at 35 tok/s. both use thinking mode. the distilled version actually has better jinja compatibility and doesn't stall midtask like base does on claude code. for conversation and reasoning it feels sharper. but for multifile autonomous coding where the model needs to coordinate 10+ files without losing track, base wins and it's not close. distillation compresses reasoning patterns but seems to lose precision on complex coordination. the model "thinks" well but can't hold the full picture across files the way base can. tested on opencode (base) and claude code (both). next up is hermes agent framework on base. same hardware. same prompt. comparing agents now, not just models. video below. first half is the distilled model's broken game. second half is what base built on the same 3090. judge for yourself.

hey if you're thinking about running qwopus (the claude opus distilled qwen 3.5 27B) as a coding agent, this might save you a few hours. i tested both the base and the distilled version on the same hardware. single RTX 3090. same prompt. same context. same everything. the only variable was the model weights. base qwen 3.5 27B built octopus invaders in 13 minutes. 1,827 lines across 11 files. zero steering. one scope bug that took 2 lines to fix. game ran. qwopus couldn't finish the same task. enemies overlapping on screen. bullets not firing. controls worked but the game was broken. i had to steer it multiple times and it still didn't produce a playable result. both run at 35 tok/s. both use thinking mode. the distilled version actually has better jinja compatibility and doesn't stall midtask like base does on claude code. for conversation and reasoning it feels sharper. but for multifile autonomous coding where the model needs to coordinate 10+ files without losing track, base wins and it's not close. distillation compresses reasoning patterns but seems to lose precision on complex coordination. the model "thinks" well but can't hold the full picture across files the way base can. tested on opencode (base) and claude code (both). next up is hermes agent framework on base. same hardware. same prompt. comparing agents now, not just models. video below. first half is the distilled model's broken game. second half is what base built on the same 3090. judge for yourself.

Sudo su

45,052 Aufrufe • vor 5 Monaten